Method and system for polymorphic algorithm-based network slice orchestration

ABSTRACT

A method, a device, and a non-transitory storage medium are described in which a polymorphic algorithm-based network slice orchestrator service is provided. The service may manage network slices based on radio access network performance metrics, core network performance metrics, network slice performance metrics, a machine learning framework, and polymorphic algorithms. The service may be applied to a multi-tier network. The service may include management of a data network access point of the network slice on a per-tier basis.

BACKGROUND

Development and design of networks present certain challenges from anetwork-side perspective and an end device perspective. For example,Centralized Radio Access Network (C-RAN) and Open Radio Access Network(O-RAN) architectures have been proposed to satisfy the increasingcomplexity, densification, and demands of end device applicationservices of a future generation network.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an exemplary system in which anexemplary embodiment of a polymorphic algorithm-based network sliceorchestrator service may be implemented;

FIG. 2 is a diagram illustrating another exemplary environment in whichan exemplary embodiment of the polymorphic algorithm-based network sliceorchestrator service may be implemented;

FIG. 3A is a diagram illustrating an exemplary components of anorchestrator device that provides an exemplary embodiment of thepolymorphic algorithm-based network slice orchestrator service;

FIG. 3B is a diagram illustrating an exemplary process of thepolymorphic algorithm-based network slice orchestrator service;

FIG. 3C is a diagram illustrating another exemplary process of thepolymorphic algorithm-based network slice orchestrator service;

FIGS. 4A-4D are diagrams illustrating still another exemplary process ofthe polymorphic algorithm-based network slice orchestrator service;

FIG. 5 is a diagram illustrating exemplary components of a device thatmay correspond to one or more of the devices illustrated and describedherein; and

FIG. 6 is a flow diagram illustrating an exemplary process of anexemplary embodiment of the polymorphic algorithm-based network sliceorchestrator service.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The following detailed description refers to the accompanying drawings.The same reference numbers in different drawings may identify the sameor similar elements. Also, the following detailed description does notlimit the invention.

The development and design of next generation wireless networks may bebased on cloud technologies, software defined networking (SDN), andnetwork function virtualization (NFV). Ubiquitous automation, networkslicing, machine learning (ML), artificial intelligence (AI), closedloop service assurance, self-healing, self-configuring, and othernetwork attributes and/or services may be integral aspects of the nextgeneration network. The next generation network may include a RAN and acore network, and perhaps other types of networks, such as a serviceand/or application layer network, a cloud network, a multi-access edgecomputing (MEC) network, and so forth.

The O-RAN Reference Architecture includes a non-real time RANIntelligent Controller (MC), a near-real-time MC, various openinterfaces (e.g., O1, A1, E2, open fronthaul interface, etc.),interoperability with standard interfaces (e.g., Third GenerationPartnership Project (3GPP) interfaces, such as F1, W1, E1, X2, Xn,etc.), open network devices (e.g., O-RAN Centralized Unit (O-CU), O-RANDistributed Unit (O-DU), O-RAN next generation Node B (O-gNB), O-RANevolved Node B (O-eNB, etc.), white box hardware, and open sourcesoftware. The hierarchical RICs may include ML models and/or AIcomponents. The control functionality of the non-real-time RIC relatesto a non-real-time timeframe and the control functionality of the nearreal-time RIC relates to a near real-time timeframe. Messages generatedfrom AI-enabled policies and ML-based models of the non-real-time RICmay be communicated to the near-real-time RIC.

Centralized and distributed algorithms that may operate in differenttime domains, and may relate to different complexities, network nodes,data sets, inputs, and outputs may not have any correlation with eachother. Given this framework, another entity may be used to coordinateand harmonize decisions for a given goal, such as optimization oranother criterion. However, the harmonization of these types ofalgorithms having different time granularities and scopes (e.g., typeand number of nodes), among other things, towards a common optimizationis non-trivial, and can result in high communication latencies due tohigh transactional velocities between these algorithms for coordination.Further, the configuration of an AI and/or ML framework that managesthese algorithms having different goal functions with an aim towardmaximizing a yield across the network is problematic.

The performance of a network slice may be reliant on multiple networks,such as the radio access network, the core network, and perhaps theapplication service layer network, and various criteria and factors,such as rendering the network slice, carrier aggregation of a device,radio quality, cell-level congestion, latency associated with the corenetwork, Transmission Control Protocol (TCP) flow control, aggregationpoint for the traffic being sourced for the network slice, reaction todynamism in the RAN and/or the core network, among other things.Currently, even though there may be dynamic instantiation of anapplication at a MEC network or other type of application service layernetwork, the analytics used to make such a determination are mostlystatic. There is also a lack of stateful associations between the corenetwork and MEC traffic instances because the traffic is flow-based(e.g., traffic type) and the granularity is not per user device. Thereis also a disassociation between that RAN and core network that pertainsto network slice management. Decisions for a MEC network or some othercentralized traffic end point may be statically provisioned andconfigured based on a priori information. Additionally, differentgranularities for network slice management may occur depending onwhether a network slice is configured for one-to-many devices or one perdevice-type, which may in turn require different stateful associations.

According to exemplary embodiments, a polymorphic algorithm-basednetwork slice orchestrator service is described. According to anexemplary embodiment, a network provides the polymorphic algorithm-basednetwork slice orchestrator service. The network may include a wirelessnetwork, a self-organizing network (SON), an O-RAN-based network, awired network, a virtual network (e.g., a virtualized RAN (vRAN)), acore network, and/or another type of network, for example. According toan exemplary embodiment, the network may include a multi-tier orhierarchical framework. For example, the network may include athree-tiered framework, which may include a centralized tier, an edgetier, and a far edge tier. According to other examples, the network mayinclude a different number of tiers (e.g., two-tiered, four-tiered,etc.) and/or different types of tiers.

According to an exemplary embodiment, the polymorphic algorithms may bedirected towards optimizing different metrics pertaining to networkslicing, as described herein. According to an exemplary embodiment, thepolymorphic algorithms of a metric may have the same goal functionacross different tiers of the network but may operate according todifferent time granularities. In this way, the polymorphicalgorithm-based network slice orchestrator service provides a goalfunction normalization (e.g., standardize the measure of the algorithmyield) among polymorphic algorithms of the same metric type. The timegranularities may be configurable (e.g., non-real-time, near real-time,real-time, and/or other types of time periods). As an example, the timegranularity of the polymorphic algorithm of a metric type at node level(e.g., at a third tier of the network) may operate at time granularitiesin seconds or milliseconds, while the time granularities of thepolymorphic algorithm of the metric type across a group of nodes (e.g.,at a second tier of the network) may operate at time granularities inseconds or minutes, and the time granularities of the polymorphicalgorithm of the metric type across a centralized infrastructure of thenetwork (e.g., at a first tier of the network) may operate at timegranularities in hours or days.

According to an exemplary embodiment, the network may include a sliceorchestrator or other type of controller device (referred to herein asan orchestrator device) that provides the polymorphic algorithm-basednetwork slice orchestrator service. According to an exemplaryembodiment, the orchestrator device may manage the execution of thepolymorphic algorithms pertaining to network slices across each tier ofthe network. For example, the orchestrator device may manage thepolymorphic algorithms, such as at node level, at a group of nodeslevel, and at a centralized level of the network, and/or another type ofconfigurable scope.

According to an exemplary embodiment, the orchestrator device may allowthe polymorphic algorithms of a metric to operate in a single tier ofthe network and its associated time granularity, as described herein.For example, for any given node or group of nodes of the network, apolymorphic algorithm of a metric type may be active in only a singletier of the network. That is, multiple instances of the polymorphicalgorithm of a metric type may not be active in multiple tiers of thenetwork for a given node or group of nodes. In this regard, for example,in a three-tiered network, the polymorphic algorithm scope for a cell ora cluster of cells or the network may be mutually exclusive such that(Scope (tier 1))∩(Scope (tier 2))∩(Scope (tier 3))==Null. So at a lowestdenomination of the network (e.g., at a node level), there may be onlyone polymorphic algorithm supervising the node from tier 1, tier 2, ortier 3 of the network. According to an exemplary embodiment, theorchestrator device may manage the transition of the polymorphicalgorithms of the metric type to operate in another tier and associatedtime granularity.

According to an exemplary embodiment, the orchestrator device may managetraffic aggregation devices of network slices associated with a network.For example, the orchestrator device may determine at what tier of thenetwork a packet gateway (PGW) device or a user plane function (UPF)device may operate for a given network slice, as well as other aspectsof the lifecycle of the PGW device, the UPF device, or other type ofdata network aggregation point (DNAP). According to an exemplaryembodiment, the orchestrator device may manage application layer devicesof network slices. For example, the orchestrator device may manage thelifecycle of a host device, a virtual entity of the host (e.g., acontainer, a virtual machine, etc.) that provides an application or aservice relating to the network slice, or a proxy device. Similar to thetraffic aggregation devices, the orchestrator device may manage thelocation (e.g., tier of the network) at which the application layerdevice may operate. In some instances, the host device or the virtualentity may be located in a network external to the network managed bythe orchestrator device. For example, the host device or the virtualentity may be located in a MEC network, the Internet, or other type ofapplication layer network. The orchestrator device may still manage thetraffic aggregation device or proxy device. Alternatively, as describedherein, the orchestrator device may include the management of theapplication layer network. A network slice may be associated withvarious types of information (e.g., Single Network Slice SelectionAssistance Information (S-NSSAI), etc.).

The orchestrator device may also provide service orchestration, networkslice management (e.g., lifecycle management, such as instantiation,deletion, modification, etc.), and flow management. As described herein,the orchestrator device may also obtain network slice parameterspertaining to performance (e.g., reliability, latency, throughput,etc.), RAN performance metrics (e.g., cell and cluster performance,etc.), and core performance metrics (e.g., congestion, flow control,etc.).

According to an exemplary embodiment, the polymorphic algorithm-basednetwork slice orchestrator may operate in various modes. For example,the various modes may include a proactive mode and a reactive mode, asdescribed herein. According to an exemplary embodiment, the polymorphicalgorithm-based network slice orchestrator service may enable an AIframework to monitor the effectiveness of the polymorphic algorithms andtheir execution at different tiers of the network based on reinforcementlearning. The AI framework may also allow information regardingpolymorphic algorithm performance to be shared for potentialcollaboration between different polymorphic algorithms.

In view of the foregoing, the polymorphic algorithm-based network sliceorchestrator service may optimize the allocation and use of networkresources to achieve an optimization or network performance targetrelating to network slices of a network. For example, the optimizationmay relate to resources and management of a RAN, core traffic management(e.g., traffic aggregation devices, application layer devices), andperhaps a MEC network.

The polymorphic algorithm-based network slice orchestrator service mayalso provide low churn by re-using the same algorithms across multipletiers of the network, albeit at different time granularities, based onvirtualization. The polymorphic algorithm-based network sliceorchestrator service may also reduce the complexities of the AI and/orML framework and the ability to manage coordination in the network basedon the goal function normalization. The polymorphic algorithm-basednetwork slice orchestrator service may also reduce the resource load byexecuting a polymorphic algorithm of a metric type at a given tier ofthe network only when necessary and on a single-tier basis, as describedherein. In this way, the polymorphic algorithm-based network sliceorchestrator service may provide scope reduction and singularity basedon reducing the number of polymorphic algorithm instances of the samemetric type executing with the same objective across multiple tiers ofthe network.

The polymorphic algorithm-based network slice orchestrator service mayalso improve the management of network slice metrics (e.g., latency,reliability, throughput, Quality of Service (QoS), Key PerformanceIndicator (KPI), Quality of Experience (QoE) score, Mean Opinion Score(MOS), etc.), network slice life-cycle (e.g., instantiation, monitoring,deletion, adaptation, and other provisioning facets) and otherattributes or performance-related issues of the network based on thecommon goal functions of the polymorphic algorithms across multipletiers of the network, the AI and/or ML framework (e.g., learned modelinputs, policies, etc.), traffic aggregation point and application layermanagement, single-tier configuration and optimization strategies (e.g.,tier-based optimization and configuration working across various timegranularities to obtain an optimal yield) that may be applied.

FIG. 1 is a diagram illustrating an exemplary system 100 in which anexemplary embodiment of the polymorphic algorithm-based network sliceorchestrator service may be implemented. As illustrated, system 100includes an algorithm repository 105, an AI/ML, framework 110, a datacollector 115, an orchestrator device 130, a virtualized infrastructuremanager (VIM) 135, a first tier polymorphic algorithms and platform 150,a second tier polymorphic algorithms and platform 155, and a third tierpolymorphic algorithms and platform 160. According to other exemplaryembodiments of system 100, multiple network devices may be combined intoa single network device. For example, orchestrator device 130 mayinclude AI/ML framework 110 and/or algorithm repository 105.Additionally, or alternatively, a single network device may beimplemented as multiple network devices in which a process or a functionmay be collaboratively performed or multiple processes or functions maybe split between them. For example, orchestrator device 130 may beimplemented to include an orchestrator device that manages networkslices, and another orchestrator device that manages the polymorphicalgorithms. Other variations of system or environment 100 may beimplemented.

The number, the type, and the arrangement of network devices illustratedin system 100 are exemplary. A network device, a network element, or anetwork function (referred to as a network device) may be implementedaccording to one or multiple network architectures, such as a clientdevice, a server device, a peer device, a proxy device, a cloud device,and/or a virtualized network device. Additionally, a network device maybe implemented according to various computing architectures, such ascentralized or distributed, and may be incorporated into various typesof network architectures (e.g., SDN, SON, virtual, logical, networkslice, wireless, wired, etc.).

The virtualization technologies and/or virtual network devices describedin relation to system 100 are also exemplary. For example, a virtualnetwork device may include a virtualized network function (VNF), aserver device, a host device, a container, a hypervisor, a virtualmachine (VM), a network function virtualization infrastructure (NFVI), anetwork function virtualization orchestrator (NFVO), a virtual networkfunction manager (VNFM), a platform manager and/or other types ofvirtualization elements, layers, hardware resources, operating systems,engines, etc.

System 100 includes communication links between the network devices.System 100 may be implemented to include wired, optical, and/or wirelesscommunication links. A communicative connection via a communication linkmay be direct or indirect. For example, an indirect communicativeconnection may involve an intermediary network device not illustrated inFIG. 1. A direct communicative connection may not involve anintermediary network device. The number and the arrangement ofcommunication links illustrated in system 100 are exemplary.

System 100 may include various planes of communication including, forexample, a control plane, a service plane, a data plane, and/or anetwork management plane. System 100 may include other types of planesof communication. A message communicated in support of the polymorphicalgorithm-based network slice orchestrator service may use at least oneof these planes of communication. Additionally, an interface of anetwork device may be modified (e.g., relative to an interface definedby a standard, such as 3GPP, International Telecommunication Union(ITU), European Telecommunications Standards Institute (ETSI), GSMAssociation (GSMA), O-RAN, etc.) or a new interface of the networkdevice may be provided in order to support the communication (e.g.,transmission and reception of messages, information elements (IE),attribute value pairs (AVPs), etc.) between network devices that supportthe polymorphic algorithm-based network slice orchestrator service, asdescribed herein. According to various exemplary implementations, theinterface of a network device may be a service-based interface, areference point-based interface, or an O-RAN interface.

Algorithm repository 105 may include a network device that storespolymorphic algorithms that relate to various optimization objectivesand/or performance metrics pertaining to a network and/or a networkslice. The polymorphic algorithms of an optimization objective and/orperformance metric may share a normalized goal function across differenttime granularities and associated tiers of a network. Additionally,according to an exemplary embodiment, the polymorphic algorithms of anoptimization objective and/or performance metric may apply the samelogic and may use the same inputs, and outputs, albeit at different timegranularities. In this way, the polymorphic algorithms may operate indifferent tiers of a network and provide a consistent goal function.Additionally, such a framework may ensure that the polymorphicalgorithms may transition between tiers of a network, enable an AI/MLframework to control sequencing of the polymorphic algorithms in termsof optimization objectives and/or performance metrics and transitioningbetween tiers, reduce the number of disparate algorithms (e.g., in termsof input/output, optimization logic, goal functions, etc.), and minimizethe complexity for management and coordination.

To enforce algorithm singularity based on polymorphism, the polymorphicalgorithms of an optimization objective and/or performance metric mayinclude certain features pertaining to their input and their output. Forexample, the input of the polymorphic algorithms may include a timegranularity profile that determines a tier of the network on which thepolymorphic algorithm operates, a validity of profile (e.g., a durationfor which the profile is valid), a goal function aggregation duration,exit criteria events, such as yield criterion (e.g., converge, diverge),time-series criterion (e.g., transient threshold), error scenariohandling, and fidelity and manifest to enable and disable. Additionally,for example, the output of the polymorphic algorithms may include a goalfunction scaling per network tier, a goal-to-yield realization, a selfconvergence check (e.g., using ML), a self assurance check (e.g., usingML), and an output profile format per time granularity profile.

According to an exemplary embodiment, the polymorphic algorithms mayrelate to self-configuring, self-optimizing, and/or self-healing. Withinthese genres, the polymorphic algorithms may include context algorithms(e.g., type of cell/sector/site, indoor, dense, urban, etc.), mobilityalgorithms (e.g., stationary, mobile, speed, direction of end device,etc.), coverage algorithms (e.g., geographic area, cell, sector, anetwork slice associated with a wireless service, a RAN device, a coredevice, and/or another type of network device (e.g., a trafficaggregation point device, an application layer device, etc.) or groupthereof of the network, etc.), quality algorithms (e.g., bit rates,latency, throughput, reliability), and/or other performance metrics(e.g., QoS, KPI, MOS, QoE, etc.) associated with a network slice, a RANdevice, a core device, another type of network device or group thereofof the network, etc.), capacity algorithms (e.g., number of users and/orapplication services supported by a cell, a sector, a network slice, aRAN device, a core device, and/or another type of network device orgroup thereof of the network, etc.), and energy algorithms (e.g., turnoff a cell or sector, provide other energy conservation mechanisms thatmay minimize energy usage of a RAN device, a core device, a networkslice, another type of network device, or group thereof of the network).The projected yield of the network and/or a network slice may becalculated based on the amount of spectrum, how the resources are beingutilized by the device types and services offered, network slices beingserviced at RAN devices, core devices, application layer devices, etc.,and other factors. RAN and core optimization, and network sliceoptimization may be orchestrated by the autonomous AI/ML framework 110and orchestrator device 130 in view of the polymorphic algorithms.

AI/ML framework 110 may include a network device that includes AI and/orML logic. For example, AI/ML framework 110 may include variouslearning-based and/or intelligence logic, such as reinforcement-basedlearning, unsupervised learning, semi-supervised learning, supervisedlearning, deep learning, artificial intelligence, and/or other types ofdevice intelligence (referred to herein as “machine learning). AI/MLframework 110 may analyze output data from first tier polymorphicalgorithms and platform 150, second tier polymorphic algorithms andplatform 155, and third tier polymorphic algorithms and platform 160 viadata collector 115 to determine whether a goal function is being metbased on policies (e.g., related to thresholds, triggers, etc.) andvarious analytical assessments (e.g., related to traffic patterns, costfunction, time-series, telemetry, network location, anomalies, etc.) tooptimize and/or reach a performance target in relation to nodes of atier of the network and/or network slices.

Data collector 115 may include a network device that collects outputdata from first tier polymorphic algorithms and platform 150, secondtier polymorphic algorithms and platform 155, and third tier polymorphicalgorithms and platform 160. The output data may relate to variousnetwork devices of a RAN, which may include RAN devices, and coredevices and/or application layer devices, as described herein.

Orchestrator device 130 may include a network device that orchestratesthe polymorphic service provided by first tier polymorphic algorithmsand platform 150, second tier polymorphic algorithms and platform 155,and third tier polymorphic algorithms and platform 160 via VIM 135.Orchestrator device 130 may orchestrate virtual instances of thepolymorphic algorithms at various tiers of the network, such asinstantiation, deletion, suspension, transition to another tier, etc.,based on feedback from AWL framework 110, policies, and goals/costfunctions pertaining to the various types of polymorphic algorithms. Thepolicies may include tier configuration policies, which may be driven byAI/ML, framework 110, for selecting the polymorphic algorithm type andradio access nodes, cluster of radio access nodes, cell, sector, etc.Additionally, the policies may include tier exit criterion policies. Forexample, the tier exit policies may include static triggers and triggerthresholds for a radio access node, a cluster of radio access nodes, orsome other grouping of nodes. In this way, a threshold or a ruleinvolving the threshold within a policy may indicate how and when apolymorphic algorithm may automatically transition from one tier toanother tier of the network, for example. Orchestrator device 130 mayalso orchestrate virtual instances of RAN devices, core devices, and/orapplication layer devices at various tiers of the network, as describedherein.

Orchestrator device 130 may monitor network slice performance (e.g., RANand core telemetry) at various time granularities, and may usepredictive ML/AI models, polymorphic algorithms, and other information(e.g., context information, etc.), as described herein, for networkslice orchestration of network resources (e.g., cell, sector, networkdevice, clusters or groups of network devices, etc.). Orchestratordevice 130 may assess radio optimization states and core networkcongestion states to determine how to handle a network slice for a cell,a cluster or group of network devices, and where, when, and how to run aDNAP (e.g., via a MEC). Orchestrator device 130 may also use radionetwork and core network optimization routines to ensure that a networkslice is satisfying performance constraints and demands (e.g., asindicated in a service level agreement). Based on a virtualizationframework, orchestrator device 130 may aggregate and disaggregatenetwork slice management at various tiers of the network (e.g., cluster,cell, sector, network device, cluster or group of network devices,cells, sectors, etc.), and manage a disaggregated network sliceobjectively, with an awareness of the overall performance metric of thenetwork slice. Orchestrator device 130 may orchestrate network sliceconfigurations across different tiers of the network based on learnedAI/ML models, and orchestrate network slice management using polymorphicalgorithms across tiers in a mutually exclusive and contention-lessmanner. Orchestrator device 130 may alter routing to a DNAP based ontriggers which may be subject to transient changes in the network, suchas RAN deficiencies and core network deficiencies (e.g., congestion,etc.), for example. Orchestrator device 130 in combination with othernetwork devices (e.g., AI/ML framework 110, polymorphic platforms 150,155, and 160, etc.) may ensure a consistent level of service provided bynetwork slices based on yield and goal functions, convergence, anddivergence factors, among other things, associated with context, time,and tier of network, and may maximize the yield for a network slice bypredicting the network tier and time granularity for a network slicemanagement entity.

VIM 135 may include a network device that controls and manages compute,storage, and network resources of an NFV Infrastructure (NFVI) thatsupport the polymorphic algorithms operating at a tier of the network byfirst tier polymorphic algorithms and platform 150, second tierpolymorphic algorithms and platform 155, and third tier polymorphicalgorithms and platform 160. VIM 135 may manages the types ofpolymorphic algorithms operating and their state based on communicationswith orchestrator device 130 and algorithm repository 105.

First tier polymorphic algorithms and platform 150 includes networkdevices that host polymorphic algorithms. For example, first tierpolymorphic algorithms and platform 150 may include one or multiplevirtualization technologies, as described herein. According to anexemplary embodiment, the polymorphic algorithms of the first tier mayoperate at a longer time granularity relative to the second tier and thethird tier. First tier polymorphic algorithms and platform 150 may alsohave a different scope (e.g., virtual network core) than the scopes ofthe second tier and the third tier, as described herein.

Second tier polymorphic algorithm and platform 155 includes networkdevices that host polymorphic algorithms. For example, second tierpolymorphic algorithms and platform 155 may include one or multiplevirtualization technologies, as described herein. According to anexemplary embodiment, the polymorphic algorithms of the second tier mayoperate at a longer time granularity relative to the third tier. Secondtier polymorphic algorithms and platform 155 may also have a differentscope (e.g., virtual network edge) than the scopes of the first tier andthe third tier, as described herein.

Third tier polymorphic algorithms and platform 160 includes networkdevices that host polymorphic algorithms. For example, third tierpolymorphic algorithms and platform 160 may include one or multiplevirtualization technologies, as described herein. According to anexemplary embodiment, the polymorphic algorithms of the third tier mayoperate at a shorter time granularity relative to the first tier and thethird tier. Third tier polymorphic algorithms and platform 160 may alsohave a different scope (e.g., virtual network far edge) than the scopesof the first tier and the third tier, as described herein.

FIG. 2 is a diagram illustrating an exemplary environment in which thepolymorphic algorithm-based network slice orchestrator service may beimplemented in a network. As illustrated, a network 200 may include acore RAN 202, an edge RAN 204, and a far edge RAN 206 that correspond todifferent tiers of the network.

Core RAN 202 may include algorithm repository 105, AWL framework 110,orchestrator device 130, first tier polymorphic algorithms and platform150, a non-real-time RAN Intelligent Controller (MC) 208, and a firsttier aggregation device 214. Non-real-time MC 208 may supportnon-real-time intelligent radio resource management, higher layerprocedure optimization, and policy optimization in a RAN. For example,non-real-time MC 208 may control and optimize various radio resources,such as network slicing associated with a 5G or future RAN, or radiobearers associated with a 4G RAN. First tier aggregation device 214 mayinclude a centralized traffic aggregation device. First tier aggregationdevice 214 may include a RAN device (e.g., CU-User Plane (CU-UP),CU-Control Plane (CU-CP), etc.), and/or a core device (e.g., a PGWdevice, a UPF device, etc.). First tier aggregation device 214 mayinclude an application layer device, such as a virtual network device(e.g., a server device, a host device, a container, a VM, etc.). Firsttier aggregation device 214 may be configured on a per slice or perservice basis, for example.

Edge RAN 204 may include second tier polymorphic algorithms and platform155 a near real-time RIC 210, and a second tier aggregation device 216.Near real-time MC 210 may support near real-time intelligent radioresource management, QoS management, connectivity management, andhandover management in a RAN. For example, near real-time MC 210 maycontrol and optimize various radio resources, such as the selection ofradio access devices (e.g., evolved Node B (eNB), a CU, a nextgeneration Node B (gNB), etc.) associated with a 4G, 5G, or future RAN.Second tier aggregation device 216 may include a RAN device and/or acore device. Second tier aggregation device 216 may include anapplication layer device. Second tier aggregation device 216 may beconfigured on a per slice or per service basis, for example.

Far edge RAN 206 may include third tier polymorphic algorithms andplatform 160, a real-time MC 212, and a third tier aggregation device218. Real-time MC 212 may support real-time intelligent radio resourcemanagement. For example, real-time MC 212 may control and optimizevarious radio resources of radio access devices (e.g., eNB, radio unit(RU), remote radio head (RRH), etc.), gNB, distributed unit (DU), etc.)associated with a 4G, 5G, or future RAN, radio resource scheduling foruplink and downlink communication with an end device, and radio signalcharacteristics (e.g., modulation, beam management, etc.). Third tieraggregation device 218 may include a RAN device and/or a core device.Third tier aggregation device 218 may include an application layerdevice. Third tier aggregation device 218 may be configured on a perslice or per service basis, for example. According to an exemplaryembodiment, the polymorphic algorithm-based network slice orchestratorservice may be implemented in an O-RAN architecture or another type ofradio access network. The number of tiers of network 200 is exemplary.

For the sake of description, although not illustrated, system 100 maymanage a RAN, a core network, and perhaps other networks (e.g., abackhaul network, a fronthaul network, an application layer network,etc.). The RAN may include one or multiple networks of one or multipletypes and technologies. For example, the RAN may be implemented toinclude a Fourth Generation (4G) RAN (e.g., an Evolved UMTS TerrestrialRadio Access Network (E-UTRAN) of a Long Term Evolution (LTE) network),a 4.5G RAN (e.g., an E-UTRAN of an LTE-Advanced (LTE-A) network), a RANof an LTE-A Pro network, a next generation RAN (e.g., a Fifth Generation(5G)-access network (5G-AN) or a 5G-RAN (referred to herein as simply a5G-RAN)), another type of future generation RAN, and/or another type ofRAN (e.g., a legacy Third Generation (3G) RAN, etc.). The RAN maycommunicate with other types of access networks, such as, for example, aWiFi network, a Worldwide Interoperability for Microwave Access (WiMAX)network, a local area network (LAN), a Citizens Broadband Radio System(CBRS) network, a cloud RAN, a wired network (e.g., optical, cable,etc.), an optical network, or another type of network that providesaccess to or can be used as an on-ramp to the RAN, a core network,and/or a MEC network, for example.

The RAN may include different and multiple functional splitting, such asoptions 1, 2, 3, 4, 5, 6, 7, or 8 that relate to combinations of the RANand a core network including an Evolved Packet Core (EPC) network and/ora NG core (NGC) network, or the splitting of the various layers (e.g.,physical layer, Media Access Control (MAC) layer, Radio Link Control(RLC) layer, and Packet Data Convergence Protocol (PDCP) layer), planesplitting (e.g., user plane, control plane, etc.), a CU, a DU, interfacesplitting (e.g., F1-U, F1-C, E1, Xn-C, Xn-U, X2-C, Common Public RadioInterface (CPRI), etc.) as well as other types of network services, suchas dual connectivity (DC) or higher (e.g., a secondary cell group (SCG)split bearer service, a master cell group (MCG) split bearer, an SCGbearer service, non-standalone (NSA), standalone (SA), etc.), CA (e.g.,intra-band, inter-band, contiguous, non-contiguous, etc.), networkslicing, coordinated multipoint (CoMP), various duplex schemes (e.g.,frequency division duplex (FDD), time division duplex (TDD), half-duplexFDD (H-FDD), etc.), and/or another type of connectivity service.

The RAN may be implemented to include various architectures of wirelessservice, such as, for example, macrocell, microcell, femtocell,picocell, metrocell, new radio (NR) cell, LTE cell, non-cell, or anothertype of cell architecture. Additionally, according to various exemplaryembodiments, the RAN may be implemented according to various wirelesstechnologies (e.g., radio access technologies (RATs), etc.), and variouswireless standards, frequencies, bands, and segments of radio spectrum(e.g., centimeter (cm) wave, millimeter (mm) wave, below 6 Gigahertz(GHz), above 6 GHz, licensed radio spectrum, unlicensed radio spectrum,etc.), and/or other attributes or technologies used for radiocommunication.

Depending on the implementation, the RAN may include one or multipletypes of network devices, such as RAN devices. For example, the RANdevices may include an eNB, a gNB, an evolved Long Term Evolution (eLTE)eNB, a radio network controller (RNC), a remote radio head (RRH), abaseband unit (BBU), a CU, a DU, a small cell node (e.g., a picocelldevice, a femtocell device, a microcell device, a home eNB, etc.), afuture generation wireless access device, another type of wireless node(e.g., a WiFi device, a WiMax device, a hotspot device, etc.) thatprovides a wireless access service, or other another type of networkdevice that provides a transport service (e.g., routing and forwarding),such as a router, a switch, or another type of layer 3 (e.g., networklayer of the Open Systems Interconnection (OSI) model) network device.Additionally, or alternatively, the RAN devices may include wired and/oroptical devices (e.g., modem, wired access point, optical access point,Ethernet device, etc.) that provide network access. According to someexemplary embodiments, the RAN devices may include O-RAN access devicesand O-RAN interfaces.

A core network may include one or multiple networks of one or multiplenetwork types and technologies. The core network may include acomplementary network of the RAN. For example, the core network may beimplemented to include an Evolved Packet Core (EPC) of an LTE, an LTE-A,an LTE-A Pro, a next generation core (NGC) network, and/or a futuregeneration core network. The core network may include a legacy corenetwork.

Depending on the implementation, the core network may include varioustypes of core devices. For example, the core devices may include amobility management entity (MME), a PGW, a serving gateway (SGW), a homesubscriber server (HSS), an authentication, authorization, andaccounting (AAA) server, a policy charging and rules function (PCRF), acharging system (CS), a UPF, an access and mobility management function(AMF), a session management function (SMF), a unified data management(UDM) device, an authentication server function (AUSF), a network sliceselection function (NSSF), a network repository function (NRF), anetwork exposure function (NEF), a policy control function (PCF), anetwork data analytics function (NWDAF), a lifecycle management (LCM)device, and/or an application function (AF). According to otherexemplary implementations, the core network may include additional,different, and/or fewer network devices than those described. Forexample, the core devices may include a non-standard and/or aproprietary network device, or another type of network device that maybe well-known but not particularly mentioned herein.

FIG. 3A is a diagram illustrating exemplary component of orchestratordevice 130. As illustrated, orchestrator device 130 may include a seeder307, a contextual configurer 309, a network slice optimizer 313, anetwork slice evaluator 315, and a network slice optimizer 322.According to some exemplary embodiments, as illustrated, components maybe associated with a proactive mode 305 or a reactive mode 320 ofoperation by orchestrator device 130. According to other exemplaryembodiments, the exemplary components illustrated and described may notrelate to a mode of operation. Orchestrator device 130 may includeadditional, fewer, and/or different modes of operation. According toother exemplary embodiments, multiple components may be combined into asingle component. Additionally, or alternatively, a single component maybe implemented as multiple components in which a process or a functionmay be collaboratively performed or multiple processes or functions maybe split between them.

Seeder 307 may include logic that identifies contextual informationpertaining to a sector, a cell, a network device, a group of networkdevices, cells, and/or sectors. For example, the contextual informationmay include traffic patterns (e.g., high volume, low volume, mediumvolume, etc.), types of users (e.g., highly mobile, stationary,moderately mobile, etc.), service area characteristics (e.g., dense,urban, suburban, rural, outdoors, indoors, etc.), application types(e.g., real-time, critical, Internet of Things (IoT), ultra-reliable,broadcast-like, etc.), time and day information (e.g., time of day, dayof week, daytime, evening, etc.), and/or other characteristics (e.g.,congestion, etc.) pertaining to wireless service and/or network slicesof the network. Seeder 307 may include logic that identifies the sector,the cell, the network device, or groups or clusters of network devices,cells, and/or sectors of relevance based on the contextual informationand associated deterministic signatures. Seeder 307 may select locationsfor aggregation devices (e.g., DNAP, core device, etc.) and applicationlayer devices in support of network slices and wireless service based onthe contextual information and associated deterministic signatures.

Contextual configurer 309 may include logic that generates a profile foreach tier of the network based on the contextual information andassociated deterministic signatures. The profile may include initial ordefault parameters and values that relate to network sliceconfigurations. For example, the network slice configurations may relateto mobility, coverage, quality, capacity, and energy associated withnetwork slices and their optimization. The profile may also includepolicies and performance targets that may manage network slices, thetransitioning between tiers, and lifecycle management of RAN, core, andapplication layer devices. Contextual configurer 309 may also assignthresholds and/or other types of configurations that may govern a modeof operation of orchestrator device 130 relating to a network slice orportion thereof. For example, for a network device, a network slice, agroup of network devices, and/or a network tier basis, orchestratordevice 130 may manage resources (e.g., network devices, communicationlinks, etc.), associated functions of network devices, and wirelessand/or application services according to a proactive mode or a reactivemode of operation. By way of further example, a threshold configurationmay trigger the use of a reactive algorithm which may cause networkresources to move from edge RAN 204 to far edge RAN 206, instantiatetraffic aggregation devices at far edge RAN 206, and/or some otherreactive measure to satisfy performance metrics of a network slice orapplication service.

Network slicer 311 may include logic that manages a lifecycle of anetwork slice. For example, network slice 311 may instantiate, delete,suspend, modify, and/or perform some other operation relative to anetwork slice and associated application service. Network slicer 311 maymanage the network slice based on inputs from other components oforchestrator device 130 (e.g., seeder 307, contextual configurer 309,etc.) and/or other network devices of the polymorphic algorithm-basednetwork slice orchestrator service (e.g., AI/ML framework 110, platforms150, 155, and 160, etc.). Network slicer 311 may also include logic thatmay manage network devices of the network slice, such as a RAN device, acore device, a DNAP, and perhaps other network devices (e.g., anapplication layer device), as described herein. The management of thenetwork device of the network slice may include lifecycle management,management of location in network, management of transition betweentiers of the network, and so forth.

Network slice optimizer 313 may include logic that identifies RAN andcore optimization states and to optimize network slice performance andunderlying performance of network devices of a tier relevant to thenetwork slice. Network slice optimizer 313 may adjust the profiles ofcontextual configurer 309 based on output from AI/ML framework 110 andnetwork slice evaluator 315, as well as other information (e.g.,contextual information, assigned profiles, etc.). Network sliceoptimizer 313 may include RAN optimization algorithms and coreoptimization algorithms that may provide optimization of network slicesand underling resources (e.g., network devices, communication links,etc.). Network slice optimizer 313 may include logic that transitionsthe execution of a polymorphic algorithm between tiers of the network,alters the location of a traffic aggregation device and/or anapplication layer device associated with a network slice or applicationservice, alters the type, number, location of RAN and/or core devices,the amount of resources, and/or alterations of other types ofconfigurations of a network slice, for example. Network slice optimizer313 may also include logic that may alter the mode of operation oforchestrator 130 in relation to a network slice, a tier of the networkpertaining to a group of network devices, an individual network device,or other network resources. For example, policies and thresholds mayprovide that after a certain number of unsuccessful attempts and/orother configurable criteria (e.g., time-based, degree of deficiency ofperformance, rate of deficiency of performance or divergence, notsatisfying a minimum performance metric threshold, etc.) to optimize anetwork slice or portion thereof in relation to a performance metric,network slice optimizer 313 may pass the optimization to network sliceoptimizer 322.

Network slice evaluator 315 may include logic that analyzes networkslice performance and determines deficiencies in performance associatedwith network slices, underlying network resources, and tiers of thenetwork. For example, network slice performance may be indicated bynetwork/network slice state information provided by first tierpolymorphic algorithms and platform 150, second tier polymorphicalgorithms and platform 155, third tier polymorphic algorithms andplatform 160. Network slice evaluator 315 may also analyze policies,performance targets, output from AI/ML framework 110, contextualinformation and/or profiles as a basis to identify problem signaturesrelating to network slice performance (e.g., latency, reliability,throughput, and/or other configurable performance metric).

Network slice evaluator 315 may include logic that provides RAN and coreperformance validations for network slices. For example, for networkdevices of relevance of a network slice and associated tier of thenetwork, network slice evaluator 315 may calculate scores or valuesrelating to performance metrics for optimization. For example, for RANoptimization, as previously mentioned, RAN optimization may includecoverage, quality, capacity, mobility, and perhaps other metrics such asenergy. Described below are exemplary expressions that may be directedto such RAN optimization calculations. The RAN parameter, such ascoverage, quality, capacity, etc., may be normalized.

According to an exemplary implementation, the following exemplaryexpression may be used to calculate a coverage score for a network sliceacross one or multiple frequency bands:Σ(Coverage_Score_(BAND)=Fn(Pathloss,TimingAdvance)_(N_TILE-SLICE)−Fn(Pathloss,Timing Advance)_(TARGET))  (1)

According to exemplary expression (1), the arguments for the function Fn( ) may include path loss and/or timing advance (TA). According to otherimplementations, the expression may include additional or differentarguments. Fn ( )_(N_TILE-SLICE) relates to the network slice and Fn( )relates to the performance target.

According to an exemplary implementation, the following exemplaryexpression may be used to calculate a quality score for a network sliceacross one or multiple frequency bands:Σ(Quality_Score_(BAND)=Fn(CQI,MIMO_(MODE))_(N_TILE-SLICE)−Fn(CQI,MIMO_(MODE))_(TARGET))  (2)

According to exemplary expression (2), the arguments for the function Fn( ) may include channel quality indicator (CQI) and/or Multiple InMultiple Out (MIMO) mode. For example, the MIMO mode may includetransmit diversity, open loop, spatial multiplexing (OL-SM), closed loopSM (CL-SM), multi-user MIMO, single antenna port, closed loop Rank 1with pre-coding, and/or other known (e.g., LTE MIMO modes) or 5G and/orfuture MIMO modes (e.g., Massive MIMO, etc.). According to otherimplementations, the expression may include additional or differentarguments. Fn ( )_(N_TILE-SLICE) relates to the network slice and Fn( )relates to the performance target.

According to an exemplary implementation, the following exemplaryexpression may be used to calculate a capacity score for a network sliceacross one or multiple frequency bands:Σ(Capacity_Score_(BAND)=Fn(SpectralEfficiency,Throughput)_(N_TILE-SLICE)−Fn(SpectralEfficiency,Throughput)_(TARGET))  (3)

According to exemplary expression (3), the arguments for the function Fn( ) may include spectral efficiency and/or throughput. According toother implementations, the expression may include additional ordifferent arguments. Fn ( )_(N_TILE-SLICE) relates to the network sliceand Fn( ) relates to the performance target.

According to an exemplary implementation, the following exemplaryexpression may be used to calculate a mobility score for a network sliceacross one or multiple frequency bands:Σ(Mobility_Score_(BAND)=Fn(Mobility State)_(N_TILE-SLICE)−Fn(MobilityState)_(TARGET))  (4)According to exemplary expression (4), the arguments for the function Fn( ) may include a mobility state. For example, the mobility state mayprovide a measure of benefit or degradation associated with mobility.For example, the mobility state may relate to the number of droppedsessions during a handover, a handover success rate, the number ofping-pong handovers, or other parameters in which mobility may influencewireless service performance, network slice performance, handoverperformance, and so forth. According to other implementations, theexpression may include additional or different arguments. Fn ()_(N_TILE-SLICE) relates to the network slice and Fn( ) relates to theperformance target.

For core performance validation, network slice evaluator 315 maycalculate core performance metrics relative to network devices ofrelevance to a network slice. Similarly, the core parameters, such asTCP flow, topology, and/or routing, may be normalized. According to anexemplary implementation, the following exemplary expression may be usedto calculate a TCP score (e.g., associated with flow control) for anetwork slice across one or multiple frequency bands:Σ(TCP_Score_(SLICE)=Fn(WindowSize,Other TCPParameter)_(SLICE)−Fn(WindowSize,Other TCP Parameter)_(TARGET))  (5)

According to exemplary expression (5), the arguments for the function Fn( ) may include TCP Window Size (e.g., receive window) and/or other TCPparameter (e.g., maximum segment size (MSS), buffer size, congestionwindow size, and/or other TCP parameters that may impact a TCP flow orother TCP performance). According to other implementations, theexpression may include additional or different arguments. Fn ( )_(SLICE)relates to the network slice and Fn( ) relates to the performancetarget.

According to an exemplary implementation, the following exemplaryexpression may be used to calculate a topology and/or routing score fora network slice across one or multiple frequency bands:Σ(Topology_Score_(BAND)=Fn(Topology Changes,RoutingReliability)_(SLICE)−Fn(Topology Changes,RoutingReliability)_(TARGET))  (6)According to exemplary expression (6), the arguments for the function Fn( ) may include topology change (e.g., switching fabric changes, hopcount, outages, etc.) and/or routing reliability. According to otherimplementations, the expression may include additional or differentarguments. Fn ( )_(SLICE) relates to the network slice and Fn( ) relatesto the performance target.

Network slice optimizer 322 may include logic that identifies RAN andcore states and to increase network slice performance and underlyingperformance of network devices of a tier relevant to the network slice.Network slice optimizer 322 may adjust the profiles of contextualconfigurer 309 based on output from AI/ML framework 110 and networkslice evaluator 315, as well as other information (e.g., contextualinformation, assigned profiles, etc.). Network slice optimizer 322 mayinclude RAN optimization algorithms and core optimization algorithmsthat may provide optimization of network slices and underling resources(e.g., network devices, communication links, etc.). Network sliceoptimizer 322 may include logic that transitions the execution of apolymorphic algorithm between tiers of the network, alters the locationof a traffic aggregation device and/or an application layer deviceassociated with a network slice or application service, alters the type,number, location of RAN and/or core devices, the amount of resources,and/or alterations of other types of configurations of a network slice,for example.

Network slice optimizer 322 may also include logic that may alter themode of operation of orchestrator device 130 in relation to a networkslice, a tier of the network pertaining to a group of network devices,an individual network device, or other network resources. For example,policies and thresholds may provide that after a certain number ofunsuccessful attempts and/or other configurable criteria (e.g.,time-based, degree of deficiency of performance, rate of deficiency ofperformance or divergence, not satisfying a minimum performance metricthreshold, etc.) to optimize a network slice or portion thereof inrelation to a performance metric, network slice optimizer 322 may notifya network administrator or other personnel. Alternatively, network sliceoptimizer 322 may alter the mode of operation of orchestrator device 130to a proactive mode when a performance metric value (e.g., a thresholdvalue, within a range of values, such minimum and maximum values, etc.)is satisfied.

According to some exemplary embodiments, network slice optimizer 322 maybe invoked when the performance metric value (e.g., a threshold value, arange of values) is not satisfied, and network slice optimizer 313 maybe invoked when the performance metric value is satisfied. According toother exemplary embodiments, the invocation of network slice optimizer313 and network slice optimizer 322 may be based on other criteria,which may or may not include satisfaction of the performance metricvalue. The performance metric value may relate to various networkdevices (e.g., RAN, core, DNAP, application layer, etc.), network slices(e.g., reliability, latency, throughput, and/or other KPIs, QoS, QoE,MOS, etc.), polymorphic algorithm optimizations (e.g., mobility,coverage, quality, capacity, energy, etc.).

FIG. 3B is a diagram illustrating an exemplary process 325 of thepolymorphic algorithm-based network slice orchestrator service. Forexample, network slice evaluator 315 may invoke a reactive mode ofoperation of orchestrator device 130 based on RAN performancevalidation, as previously described.

In block 330, orchestrator device 130 may identify a RAN performancemetric below a threshold score. In block 333, orchestrator device 130may employ a reactive polymorphic algorithm of the identified RANperformance metric for a target network device of relevance to a networkslice and associated tier of network. In block 335, orchestrator device130 may transition a DNAP of the network slice to a different tier ofthe network. In block 337, it may be determined whether the RANperformance metric is met. For example, subsequent adjustments to thetarget network device based on the reactive polymorphic algorithm andthe DNAP, orchestrator device 130 may determine whether the RANperformance metric satisfies the threshold score. The reactivepolymorphic algorithm type may alter parameter or configuration valuesof a profile relative to those of a proactive algorithm instance.

When it is determined that the threshold score is not satisfied (block337—NO), it may be determined whether to continue the reactive procedure(block 340). For example, the reactive procedure may be implemented fora particular length of time, a certain number of iterations, and/oraccording to other criteria (e.g., rate of improvement, rate of decline,etc.). When it is determined to continue the reactive procedure (block340—YES), process 325 may return to block 333 in which the reactivepolymorphic algorithm may continue to optimize, as described herein(block 343). Orchestrator 130 may or may not also further modify theDNAP. When it is determined to not continue the reactive procedure(block 340—NO), orchestrator device 130 may notify network personnel(block 345). For example, orchestrator device 130 may generate an alertor other type of message, and transmit the alert or message to asuitable device and/or user (e.g., network personnel).

When it is determined that the threshold score is satisfied (block337—YES), it may be determined whether another RAN performance metric isto be addressed (block 347). As previously described, the polymorphicalgorithm may relate to self-configuring, self-optimizing, and/orself-healing genres. Within these genres or categories, the polymorphicalgorithms may include context algorithms, mobility algorithms, coveragealgorithms, quality algorithms, capacity algorithms, and energyalgorithms. The polymorphic algorithm-based network slice orchestratorservice may not manage, for example, the same cell sites from multipletiers using the same type of polymorphic algorithms. The polymorphicalgorithms may attain optimization or a performance target byorchestrating the order of the polymorphic algorithms based on theirtype. For example, orchestrator device 130 may select to initiallyoperate a context algorithm, followed by a mobility algorithm, acoverage algorithm, a quality algorithm, a capacity algorithm, and anenergy algorithm. According to other exemplary embodiments, the order ofthe type of polymorphic algorithms may be different. Additionally, forexample, the polymorphic algorithms may include proactive polymorphicalgorithms and reactive polymorphic algorithms. For example, proactivepolymorphic algorithms may set or establish initial or default values ofa profile, and reactive polymorphic algorithms may adjust or modifyinitial or default values responsive to policies and/or thresholds beingbreached and the transient nature of the network. According to anexemplary embodiment, when orchestrator device 130 invokes a reactivepolymorphic algorithm or a proactive polymorphic algorithm of aparticular type (e.g., context, coverage, etc.), orchestrator device 130may relinquish control of a network device or network devices ofrelevance to the network slice to the reactive polymorphic algorithm orthe proactive polymorphic algorithm. That is, orchestrator device 130may not make any changes to the network device/tier of relevance to thenetwork slice.

Additionally, according to an exemplary embodiment, the transition ofrunning one type of polymorphic to another type may be dependent onsuccessful optimization or attainment of a performance target related tothe polymorphic algorithm type, a configured time period, a failure tooptimize or attain the performance target, and/or some other criterion.As an example, if a goal function of a polymorphic algorithm type isregressing (e.g., at far edge RAN 206) for mobility, the polymorphicalgorithm-based network slice orchestrator service may not progress to acoverage algorithm, but may back out and go to a next group of cellsrelating to mobility. Depending on the number and/or order of the RANperformance metric, block 347 may or may not have RAN performancemetric. Referring to FIG. 3B, when it is determined that there isanother RAN performance metric to address (block 347—YES), process 325may proceed to block 330. However, when it is determined that there isnot another RAN performance metric to address (block 347—NO), process325 may end (block 353).

FIG. 3B illustrates an exemplary process 325, however, according toother embodiments, process 325 may include additional operations, feweroperations, and/or different operations than those illustrated in FIG.3B, and described herein. For example, process 325 may omit block 333 orblock 335 in one or more iterations.

FIG. 3C is a diagram illustrating an exemplary process 357 of thepolymorphic algorithm-based network slice orchestrator service. Forexample, network slice evaluator 315 may invoke a reactive mode ofoperation of orchestrator device 130 based on core performancevalidation, as previously described.

In block 360, orchestrator device 130 may identify a core performancemetric below a threshold score. In block 363, orchestrator device 130may employ a reactive polymorphic algorithm of the identified coreperformance metric for a target network device of relevance to a networkslice and associated tier of network. In block 365, orchestrator device130 may transition a DNAP of the network slice to a different tier ofthe network. In block 367, it may be determined whether the coreperformance metric is met. For example, subsequent adjustments to thetarget network device based on the reactive polymorphic algorithm andthe DNAP, orchestrator device 130 may determine whether the coreperformance metric satisfies the threshold score. The reactivepolymorphic algorithm type may alter parameter or configuration valuesrelative to those of a proactive algorithm instance.

When it is determined that the threshold score is not satisfied (block367—NO), it may be determined whether to continue the reactive procedure(block 370). For example, the reactive procedure may be implemented fora particular length of time, a certain number of iterations, and/oraccording to other criteria (e.g., rate of improvement, rate of decline,etc.). When it is determined to continue the reactive procedure (block370—YES), process 357 may return to block 363 in which the reactivepolymorphic algorithm may continue to optimize, as described herein(block 373). Orchestrator 130 may or may not also further modify theDNAP. When it is determined to not continue the reactive procedure(block 370—NO), orchestrator device 130 may notify network personnel(block 375). For example, orchestrator device 130 may generate an alertor other type of message, and transmit the alert or message to asuitable device.

When it is determined that the threshold score is satisfied (block367—YES), process 357 may end (block 377). Process 357 may be performedin relation to a core performance metric (e.g., topology) in parallelwith a different core performance metric (e.g., routing or TCPperformance). According to other examples, process 357 may address coreperformances according to a particular sequence. According to such animplementation, process 357 may include a step similar to that describedin block 347 of process 325.

FIG. 3C illustrates an exemplary process 357, however, according toother embodiments, process 357 may include additional operations, feweroperations, and/or different operations than those illustrated in FIG.3C, and described herein. For example, process 357 may omit block 363 orblock 365 in one or more iterations.

FIGS. 4A-4D are diagrams illustrating an exemplary process of anexemplary embodiment of the polymorphic algorithm-based network sliceorchestrator service. As illustrated, orchestrator device 130 mayreceive various inputs, such as core inputs 403, RAN inputs 405,slice/service scope inputs 407, ML model and prediction inputs 410, andnetwork slice generation trigger inputs 413. An input may be of a perslice basis. An input may be of a network device of relevance, of a tierof the network and associated time granularity, and so forth. As anexample, core inputs 403 may include information pertaining tocongestion state, route congestion, flow state and/or statistics,network topology and/or outages; RAN inputs 405 may include informationpertaining to context, optimization state, and performance;slice/service scope inputs 407 may include information pertaining to abit rate (e.g., guaranteed bit rate (GBR), maximum bit rate (MBR),non-GBR, aggregate MBR (AMBR, etc.), latency, reliability, throughput,and/or other types of QoS, KPIs); ML model and prediction inputs 410 mayinclude information pertaining to a network slice, policies, and/or timegranularities, a trained machine learning model of relevance to thenetwork slice and/or application service, anomaly detection; and networkslice generation trigger inputs 413 may include information pertainingto network slice constraints for new or existing network slices (e.g.,latency, reliability, throughput, and/or other QoS, KPIs; policies,threshold values, and/or other types of configurations), and informationpertaining to network slice generational inputs (e.g., end devicerequests for a network slice, network-based requests for a networkslice, etc.). As further illustrated, AI/ML, framework 110 may includeslice/service scope inputs 407 (and potential other inputs illustratedin FIG. 4A), which may be used as a basis to provide ML model andprediction inputs 410 to orchestrator device 130. Additionally, asdescribed herein, AWL framework 110 may obtain polymorphic algorithminputs 415, which may relate to proactive and/or reactive polymorphicalgorithms. Polymorphic algorithm inputs 415 may include informationpertaining to a goal or yield function (e.g., convergence, divergence,etc.).

Referring to FIG. 4B, orchestrator device 130 may provide variousoutputs based on the inputs received. For example, orchestrator device420 may provide a configuration output 420 relating to network sliceconfiguration 415, a slice profile output 430 relating to reactive slicemanagement 425, and slice output 440 relating to proactive slicemanagement 435. Although not illustrated in FIG. 4B, based on the inputsreceived, exemplary components of orchestrator device 130, such as thoseillustrated and described herein, may provide outputs to each other.

Configuration output 420 may include information for (proactively)configuring a network slice. For example, the configuration may includedefault or initial configurations of the network slice. Theconfiguration may relate to RAN devices, core devices, DNAP devices,and/or application layer devices. Slice output 430 may includeinformation for (reactively) optimizing a network slice. The reactiveoptimization information may pertain to RAN devices, core devices, DNAPdevices, and/or application layer devices, as described herein. Forexample, the reactive optimization information may provide for radioscheduler adjustments, radio resource configuration, beam steering,other radio and/or core network optimizations (e.g., relating to flowstate, congestion, topology, routing, etc.) associated with reactivepolymorphic algorithms, tier transition and/or modification of DNAP,and/or reactive configuration adjustments directed to optimization ofthe network slice and/or network device of relevance, as describedherein. Slice output 440 may include information for (proactively)optimizing a network slice. The proactive optimization information mayalso pertain to RAN devices, core devices, DNAP devices, and/orapplication layer devices, as described herein. For example, theproactive optimization information may provide for radio scheduleradjustments, radio resource configuration, beam steering, other radioand/or core network optimizations associated with proactive polymorphicalgorithms, tier transition and/or modification of DNAP, and/orproactive configuration adjustments of a profile directed tooptimization of the network slice and/or network device of relevance, asdescribed herein.

FIG. 4C further illustrates this portion of process 400. For example, instep 445, the network slice evaluator may apply various analytics, whichmay include determining whether a threshold has been breached relativeto a network slice (illustrated as step 450). When the network sliceevaluator determines that the threshold has not been breached (step450—NO), in step 455, the proactive network slice optimizer may generateslice output. When the network slice evaluator determines that thethreshold has been breached (step 450—YES), in step 460, the reactivenetwork slice optimizer may generate slice output 460. FIG. 4Dillustrates exemplary information included in slice output 430 and sliceoutput 440. Additionally, as illustrated, orchestrator 130 may provideDNAP output 470 pertaining to DNAP management 465. For example, the DNAPoutput 470 may include information to instantiate a DNAP, manage itslifecycle, and/or manage optimization (e.g., proactively andreactively).

FIG. 5 is a diagram illustrating exemplary components of a device 500that may be included in one or more of the devices described herein. Forexample, device 500 may correspond to AI/ML, framework 110, datacollector 115, orchestrator device 130, VIM 135, first tier polymorphicalgorithms and platform 150, second tier polymorphic algorithms andplatform 155, third tier polymorphic algorithms and platform 160,non-real-time RIC 208, near real-time RIC 210, real-time RIC 212, and/orother types of network devices, as described herein. As illustrated inFIG. 5, device 500 includes a bus 505, a processor 510, a memory/storage515 that stores software 520, a communication interface 525, an input530, and an output 535. According to other embodiments, device 500 mayinclude fewer components, additional components, different components,and/or a different arrangement of components than those illustrated inFIG. 5 and described herein.

Bus 505 includes a path that permits communication among the componentsof device 500. For example, bus 505 may include a system bus, an addressbus, a data bus, and/or a control bus. Bus 505 may also include busdrivers, bus arbiters, bus interfaces, clocks, and so forth.

Processor 510 includes one or multiple processors, microprocessors, dataprocessors, co-processors, graphics processing units (GPUs), applicationspecific integrated circuits (ASICs), controllers, programmable logicdevices, chipsets, field-programmable gate arrays (FPGAs), applicationspecific instruction-set processors (ASIPs), system-on-chips (SoCs),central processing units (CPUs) (e.g., one or multiple cores),microcontrollers, neural processing unit (NPUs), and/or some other typeof component that interprets and/or executes instructions and/or data.Processor 510 may be implemented as hardware (e.g., a microprocessor,etc.), a combination of hardware and software (e.g., a SoC, an ASIC,etc.), may include one or multiple memories (e.g., cache, etc.), etc.

Processor 510 may control the overall operation or a portion ofoperation(s) performed by device 500. Processor 510 may perform one ormultiple operations based on an operating system and/or variousapplications or computer programs (e.g., software 520). Processor 510may access instructions from memory/storage 515, from other componentsof device 500, and/or from a source external to device 500 (e.g., anetwork, another device, etc.). Processor 510 may perform an operationand/or a process based on various techniques including, for example,multithreading, parallel processing, pipelining, interleaving, etc.

Memory/storage 515 includes one or multiple memories and/or one ormultiple other types of storage mediums. For example, memory/storage 515may include one or multiple types of memories, such as, a random accessmemory (RAM), a dynamic random access memory (DRAM), a static randomaccess memory (SRAM), a cache, a read only memory (ROM), a programmableread only memory (PROM), an erasable PROM (EPROM), an electrically EPROM(EEPROM), a single in-line memory module (SIMM), a dual in-line memorymodule (DIMM), a flash memory (e.g., 2D, 3D, NOR, NAND, etc.), a solidstate memory, and/or some other type of memory. Memory/storage 515 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, a solid state disk, etc.), a Micro-ElectromechanicalSystem (MEMS)-based storage medium, and/or a nanotechnology-basedstorage medium. Memory/storage 515 may include drives for reading fromand writing to the storage medium.

Memory/storage 515 may be external to and/or removable from device 500,such as, for example, a Universal Serial Bus (USB) memory stick, adongle, a hard disk, mass storage, off-line storage, or some other typeof storing medium (e.g., a compact disk (CD), a digital versatile disk(DVD), a Blu-Ray disk (BD), etc.). Memory/storage 515 may store data,software, and/or instructions related to the operation of device 500.

Software 520 includes an application or a program that provides afunction and/or a process. As an example, with reference to system 100and/or other network devices, software 520 may include an applicationthat, when executed by processor 510, provides a function of thepolymorphic algorithm-based network slice orchestrator service, asdescribed herein. Software 520 may also include firmware, middleware,microcode, hardware description language (HDL), and/or other form ofinstruction. Software 520 may also be virtualized. Software 520 mayfurther include an operating system (OS) (e.g., Windows, Linux, Android,proprietary, etc.).

Communication interface 525 permits device 500 to communicate with otherdevices, networks, systems, and/or the like. Communication interface 525includes one or multiple wireless interfaces and/or wired interfaces.For example, communication interface 525 may include one or multipletransmitters and receivers, or transceivers. Communication interface 525may operate according to a protocol stack and a communication standard.Communication interface 525 may include an antenna. Communicationinterface 525 may include various processing logic or circuitry (e.g.,multiplexing/de-multiplexing, filtering, amplifying, converting, errorcorrection, application programming interface (API), etc.).Communication interface 525 may be implemented as a point-to-pointinterface, a service based interface, or a reference interface, forexample.

Input 530 permits an input into device 500. For example, input 530 mayinclude a keyboard, a mouse, a display, a touchscreen, a touchlessscreen, a button, a switch, an input port, speech recognition logic,and/or some other type of visual, auditory, tactile, etc., inputcomponent. Output 535 permits an output from device 500. For example,output 535 may include a speaker, a display, a touchscreen, a touchlessscreen, a light, an output port, and/or some other type of visual,auditory, tactile, etc., output component.

As previously described, a network device may be implemented accordingto various computing architectures and according to various networkarchitectures (e.g., a virtualized function, etc.). Device 500 may beimplemented in the same manner. For example, device 500 may beinstantiated, created, deleted, or some other operational state duringits life-cycle (e.g., refreshed, paused, suspended, rebooted, or anothertype of state or status), using well-known virtualization technologies(e.g., hypervisor, container engine, virtual container, virtual machine,etc.) in a RAN network and/or another type of network (e.g., a corenetwork, an application layer service network, a MEC network, etc.).

Device 500 may perform a process and/or a function, as described herein,in response to processor 510 executing software 520 stored bymemory/storage 515. By way of example, instructions may be read intomemory/storage 515 from another memory/storage 515 (not shown) or readfrom another device (not shown) via communication interface 525. Theinstructions stored by memory/storage 515 cause processor 510 to performa process described herein. Alternatively, for example, according toother implementations, device 500 performs a process described hereinbased on the execution of hardware (processor 510, etc.).

FIG. 6 is a flow diagram illustrating an exemplary process 600 of anexemplary embodiment of the polymorphic algorithm-based network sliceorchestrator service. According to an exemplary embodiment, orchestratordevice 130 may perform the steps of process 600. According to anexemplary implementation, processor 510 executes software 520 to performa step of process 600, as described herein. Alternatively, a step may beperformed by execution of only hardware. Process 600 may be performediteratively.

Referring to FIG. 6, in block 605, orchestrator device 130 mayinstantiate a network slice based on polymorphic algorithms of amulti-tier network, which includes a RAN network and a core network, andmachine learning analytics. For example, orchestrator device 130 mayreceive a request for a network slice. Orchestrator device 130 maycreate a network slice (e.g., for an end device) based on a profile,policies, polymorphic algorithms (e.g., context information, etc.), andso forth, as described herein.

In block 610, orchestrator device 130 may receive network and machinelearning inputs. For example, orchestrator device 130 may receiveinformation pertaining to a RAN network, a core network, and the networkslice, as well as inputs from AWL framework 110.

In block 615, orchestrator device 130 may evaluate a state of thenetwork slice. For example, orchestrator device 130 may analyze thereceived inputs, as described herein. For example, orchestrator device130 may provide RAN and core performance validations and network sliceperformance validation.

In block 620, it may be determined whether a threshold of the networkslice is breached. For example, orchestrator device 130 may compare avalue of the received inputs to a threshold value of the network sliceand/or a network device of relevance of the network slice.

When it is determined that the threshold of the network slice is notbreached (block 620—NO), orchestrator device 130 may invoke proactiveoptimization measures (block 625). For example, orchestrator device 130may manage the execution of polymorphic algorithms (e.g., proactivepolymorphic algorithms) and/or the management of DNAP. Orchestratordevice 130 may identify a tier of the multi-tier network (e.g., anetwork device of relevance of the network slice) to which the proactiveoptimization measures may be directed. Process 600 may continue to block610.

When it is determined that the threshold of the network slice isbreached (block 620—YES), orchestrator device 130 may determine whethera reactive parameter is breached (block 630). For example, orchestratordevice 130 may determine whether a policy that manages the reactive mode(e.g., number of iterations, rate of divergence, or other policy asdescribed herein) is satisfied.

When it is determined that the reactive measure is not breached (block630—NO), orchestrator device 130 may invoke reactive optimizationmeasures (block 635). For example, orchestrator device 130 may managethe execution of polymorphic algorithms (e.g., reactive polymorphicalgorithms) and/or the management of DNAP. Orchestrator device 130 mayidentify a tier of the multi-tier network (e.g., a network device ofrelevance of the network slice) to which the reactive optimizationmeasures may be directed. Process 600 may continue to block 610.

When it is determined that the reactive measure is breached (block630—YES), orchestrator device 130 may invoke a notification procedure(block 640). For example, orchestrator device 130 may generate andtransmit a message to a network device (e.g., an operations,administration, and maintenance (OAM) device), generate an alarm, oranother type of notification to a device and/or network personnel. Themessage or alert, for example, may indicate a network failure, and maycause subsequent human intervention.

FIG. 6 illustrates an exemplary process 600 of the polymorphicalgorithm-based network slice orchestrator service, however, accordingto other embodiments, process 600 may include additional operations,fewer operations, and/or different operations than those illustrated inFIG. 6, and described herein.

As set forth in this description and illustrated by the drawings,reference is made to “an exemplary embodiment,” “an embodiment,”“embodiments,” etc., which may include a particular feature, structureor characteristic in connection with an embodiment(s). However, the useof the phrase or term “an embodiment,” “embodiments,” etc., in variousplaces in the specification does not necessarily refer to allembodiments described, nor does it necessarily refer to the sameembodiment, nor are separate or alternative embodiments necessarilymutually exclusive of other embodiment(s). The same applies to the term“implementation,” “implementations,” etc.

The foregoing description of embodiments provides illustration, but isnot intended to be exhaustive or to limit the embodiments to the preciseform disclosed. Accordingly, modifications to the embodiments describedherein may be possible. For example, various modifications and changesmay be made thereto, and additional embodiments may be implemented,without departing from the broader scope of the invention as set forthin the claims that follow. The description and drawings are accordinglyto be regarded as illustrative rather than restrictive. As an example,the polymorphic algorithm-based network slice orchestrator service maybe applied to any information system that includes multiple functionalelements managed by a multi-tiered system (e.g., having differentgranularities) in which singular algorithms can manifest at differenttiers in an autonomous manner.

The terms “a,” “an,” and “the” are intended to be interpreted to includeone or more items. Further, the phrase “based on” is intended to beinterpreted as “based, at least in part, on,” unless explicitly statedotherwise. The term “and/or” is intended to be interpreted to includeany and all combinations of one or more of the associated items. Theword “exemplary” is used herein to mean “serving as an example.” Anyembodiment or implementation described as “exemplary” is not necessarilyto be construed as preferred or advantageous over other embodiments orimplementations.

In addition, while series of blocks have been described with regard tothe processes illustrated in the Figures, the order of the blocks may bemodified according to other embodiments. Further, non-dependent blocksmay be performed in parallel. Additionally, other processes described inthis description may be modified and/or non-dependent operations may beperformed in parallel.

Embodiments described herein may be implemented in many different formsof software executed by hardware. For example, a process or a functionmay be implemented as “logic,” a “component,” or an “element.” Thelogic, the component, or the element, may include, for example, hardware(e.g., processor 510, etc.), or a combination of hardware and software(e.g., software 520).

Embodiments have been described without reference to the specificsoftware code because the software code can be designed to implement theembodiments based on the description herein and commercially availablesoftware design environments and/or languages. For example, varioustypes of programming languages including, for example, a compiledlanguage, an interpreted language, a declarative language, or aprocedural language may be implemented.

Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote anypriority, precedence, or order of one claim element over another, thetemporal order in which acts of a method are performed, the temporalorder in which instructions executed by a device are performed, etc.,but are used merely as labels to distinguish one claim element having acertain name from another element having a same name (but for use of theordinal term) to distinguish the claim elements.

Additionally, embodiments described herein may be implemented as anon-transitory computer-readable storage medium that stores data and/orinformation, such as instructions, program code, a data structure, aprogram module, an application, a script, or other known or conventionalform suitable for use in a computing environment. The program code,instructions, application, etc., is readable and executable by aprocessor (e.g., processor 510) of a device. A non-transitory storagemedium includes one or more of the storage mediums described in relationto memory/storage 515. The non-transitory computer-readable storagemedium may be implemented in a centralized, distributed, or logicaldivision that may include a single physical memory device or multiplephysical memory devices spread across one or multiple network devices.

To the extent the aforementioned embodiments collect, store or employpersonal information of individuals, it should be understood that suchinformation shall be collected, stored, and used in accordance with allapplicable laws concerning protection of personal information.Additionally, the collection, storage and use of such information can besubject to consent of the individual to such activity, for example,through well known “opt-in” or “opt-out” processes as can be appropriatefor the situation and type of information. Collection, storage and useof personal information can be in an appropriately secure mannerreflective of the type of information, for example, through variousencryption and anonymization techniques for particularly sensitiveinformation.

No element, act, or instruction set forth in this description should beconstrued as critical or essential to the embodiments described hereinunless explicitly indicated as such.

All structural and functional equivalents to the elements of the variousaspects set forth in this disclosure that are known or later come to beknown are expressly incorporated herein by reference and are intended tobe encompassed by the claims.

What is claimed is:
 1. A method comprising: instantiating, by a device,a network slice based on polymorphic algorithms of a multi-tier network,which includes a radio access network and a core network, and a machinelearning framework; receiving, by the device, inputs pertaining to themulti-tier network and the machine learning framework; wherein thepolymorphic algorithms operate at each tier of the multi-tier networkand each polymorphic algorithm of a tier operates at a different timegranularity relative to each polymorphic algorithm of a different tier;evaluating, by the device based on the inputs, a state of the networkslice; determining, by the device, whether a threshold of the networkslice has been exceeded; and invoking, by the device, a proactiveoptimization or a reactive optimization of the network slice based on aresult of the determining.
 2. The method of claim 1, wherein thereactive optimization includes transitioning a data network access pointof the network slice from a first tier to a second tier of themulti-tier network.
 3. The method of claim 1, wherein the inputs fromthe multi-tier network include a first input pertaining to a firstnetwork device of the core network and the network slice, a second inputpertaining to a second network device of the radio access network andthe network slice, and a third input pertaining to a performance metricof the network slice.
 4. The method of claim 1, wherein one of thepolymorphic algorithms operating at each tier of the multi-tier networkis at least one of the following: a context algorithm; a mobilityalgorithm; a coverage algorithm; a quality algorithm; a capacityalgorithm; or an energy algorithm.
 5. The method of claim 1, wherein theinputs of the machine learning framework pertain to a trained machinelearning model and anomaly detection.
 6. The method of claim 1, whereinthe evaluating further comprises: calculating, by the device, a firstoptimization state value pertaining to a first network device of thecore network and the network slice; and calculating, by the device, asecond optimization state value pertaining to a second network device ofthe radio access network and the network slice.
 7. The method of claim6, wherein the first optimization state value pertains to two or more offlow control, routing reliability, or network topology, and wherein thesecond optimization state value pertains to mobility, coverage, quality,and capacity.
 8. The method of claim 1, wherein the invoking furthercomprises: identifying, by the device, a tier of the multi-tier networkto which the proactive optimization or the reactive optimization isdirected.
 9. The method of claim 1, wherein, when determining that thethreshold of the network slice has not been breached, the invokingcomprises invoking the proactive optimization, and the proactiveoptimization includes optimizing configuration values of a profilepertaining to the network slice.
 10. A device of a multi-tier networkcomprising: a processor, wherein the processor is configured to:instantiate a network slice based on polymorphic algorithms of amulti-tier network, which includes a radio access network and a corenetwork, and a machine leaning framework; receive inputs pertaining tothe multi-tier network and the machine learning framework; wherein thepolymorphic algorithms operate at each tier of the multi-tier networkand each polymorphic algorithm of a tier operates at a different timegranularity relative to each polymorphic algorithm of a different tier;evaluate, based on the inputs, a state of the network slice; determinewhether a threshold of the network slice has been breached; and invoke aproactive optimization or a reactive optimization of the network slicebased on a result of the determination.
 11. The device of claim 10,wherein the reactive optimization includes transitioning a data networkaccess point of the network slice from a first tier to a second tier ofthe multi-tier network.
 12. The device of claim 10, wherein the inputsfrom the multi-tier network include a first input pertaining to a firstnetwork device of the core network and the network slice, a second inputpertaining to a second network device of the radio access network andthe network slice, and a third input pertaining to a performance metricof the network slice.
 13. The device of claim 10, wherein one of thepolymorphic algorithms operating at each tier of the multi-tier networkis at least one of the following: a context algorithm; a mobilityalgorithm: a coverage algorithm; a quality algorithm; a capacityalgorithm; or an energy algorithm.
 14. The device of claim 10, whereinthe inputs of the machine learning framework pertain to a trainedmachine learning model and anomaly detection.
 15. The device of claim10, wherein when evaluating, the processor is further configured to:calculate a first optimization state value pertaining to a first networkdevice of the core network and the network slice; and calculate a secondoptimization state value pertaining to a second network device of theradio access network and the network slice.
 16. The device of claim 15,wherein the first optimization state value pertains to two or more offlow control, routing reliability, or network topology, and wherein thesecond optimization state value pertains to mobility, coverage, quality,and capacity.
 17. The device of claim 10, wherein the processor isfurther configured to: identify a tier of the multi-tier network towhich the proactive optimization or the reactive optimization isdirected.
 18. A non-transitory computer-readable storage medium storinginstructions executable by a processor of a device of a multi-tiernetwork, which when executed cause the device to: instantiate a networkslice based on polymorphic algorithms of a multi-tier network, whichincludes a radio access network and a core network, and a machinelearning framework; receive inputs pertaining to the multi-tier networkand the machine learning framework; wherein the polymorphic algorithmsoperate at each tier of the multi-tier network and each polymorphicalgorithm of a tier operates at a different time granularity relative toeach polymorphic algorithm of a different tier; evaluate, based on theinputs, a state of the network slice: determine whether a threshold ofthe network slice has been breached; and invoke a proactive optimizationor a reactive optimization of the network slice based on a result of thedetermination.
 19. The non-transitory computer-readable storage mediumof claim 18, wherein the reactive optimization includes transitioning adata network access point of the network slice from a first tier to asecond tier of the multi-tier network.
 20. The non-transitorycomputer-readable storage medium of claim 18, wherein the instructionsto evaluate further comprising instructions to: calculate a firstoptimization state value pertaining to a first network device of thecore network and the network slice; and calculate a second optimizationstate value pertaining to a second network device of the radio accessnetwork and the network slice, wherein the first optimization statevalue pertains to two or more of flow control routing reliability, ornetwork topology, and wherein the second optimization state valuepertains to mobility, coverage, quality, and capacity.